A Hybrid Model Associating Population Pharmacokinetics with Machine Learning: A Case Study with Iohexol Clearance Estimation

• Published in: Clinical pharmacokinetics Vol. 61; no. 8; pp. 1157 - 1165
• Main Authors: Destere, Alexandre, Marquet, Pierre, Gandonnière, Charlotte Salmon, Åsberg, Anders, Loustaud-Ratti, Véronique, Carrier, Paul, Ehrmann, Stephan, Guellec, Chantal Barin-Le, Premaud, Aurélie, Woillard, Jean-Baptiste
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2022; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Bias; Case studies; Datasets; Drug dosages; Internal Medicine; Kidney transplants; Life Sciences; Machine learning; Medicine; Medicine & Public Health; Original Research Article; Patients; Pharmaceutical sciences; Pharmacokinetics; Pharmacology; Pharmacology/Toxicology; Pharmacotherapy; Population; Simulation; Values; iohexol; Bayesian estimator; glomerular filtration rate; Machine Learning; iohexol population pharmacokinetics Bayesian estimator glomerular filtration rate Machine Learning; population pharmacokinetics
• ISSN: 0312-5963; 1179-1926
• DOI: 10.1007/s40262-022-01138-x

Background Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms. Objective: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation. Methods The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients. Results The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset). Conclusions  In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone.